lavaan: An R package for structural equation modeling. Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.

References in zbMATH (referenced in 53 articles , 1 standard article )

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  1. Boudt, Kris; Cornilly, Dries; Verdonck, Tim: Nearest comoment estimation with unobserved factors (2020)
  2. Calcagnì, Antonio; Lombardi, Luigi; Avanzi, Lorenzo; Pascali, Eduardo: Multiple mediation analysis for interval-valued data (2020)
  3. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  4. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  5. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  6. Drton, Mathias; Fox, Christopher; Wang, Y. Samuel: Computation of maximum likelihood estimates in cyclic structural equation models (2019)
  7. Foldnes, Njål; Grønneberg, Steffen: On identification and non-normal simulation in ordinal covariance and item response models (2019)
  8. Gana, Kamel; Broc, Guillaume: Structural equation modeling with lavaan (2019)
  9. Grønneberg, Steffen; Foldnes, Njål: A problem with discretizing Vale-Maurelli in simulation studies (2019)
  10. Hong, Maxwell R.; Jacobucci, Ross: Book review of: K. J. Grimm et al., Growth modeling. Structural equation and multilevel modeling approaches (2019)
  11. Lai, Keke: Creating misspecified models in moment structure analysis (2019)
  12. Merkle, Edgar C.; Furr, Daniel; Rabe-Hesketh, Sophia: Bayesian comparison of latent variable models: conditional versus marginal likelihoods (2019)
  13. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  14. Papageorgiou, Ioulia; Moustaki, Irini: Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables (2019)
  15. Paul-Christian Burkner: thurstonianIRT: Thurstonian IRT Models in R (2019) not zbMATH
  16. Ranger, Jochen; Wolgast, Anett; Kuhn, Jörg-Tobias: Robust estimation of the hierarchical model for responses and response times (2019)
  17. Segal, Brian D.; Braun, Thomas; Gonzalez, Richard; Elliott, Michael R.: Tests of matrix structure for construct validation (2019)
  18. Sengewald, Marie-Ann; Pohl, Steffi: Compensation and amplification of attenuation bias in causal effect estimates (2019)
  19. Usami, Satoshi; Jacobucci, Ross; Hayes, Timothy: The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories (2019)
  20. Wang, Chun; Xu, Gongjun; Zhang, Xue: Correction for item response theory latent trait measurement error in linear mixed effects models (2019)

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